https://github.com/alihassanml/difference-between-classfication-and-regression

In machine learning, regression and classification are two types of supervised learning tasks.
https://github.com/alihassanml/difference-between-classfication-and-regression

Last synced: 8 months ago
JSON representation

In machine learning, regression and classification are two types of supervised learning tasks.

• Host: GitHub
• URL: https://github.com/alihassanml/difference-between-classfication-and-regression
• Owner: alihassanml
• License: mit
• Created: 2024-07-17T17:28:31.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-07-17T17:48:25.000Z (about 1 year ago)
• Last Synced: 2025-01-01T15:11:46.897Z (9 months ago)
• Language: Jupyter Notebook
• Size: 9.77 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

          
# Difference Between Classfiaction and Regression

In machine learning, regression and classification are two types of supervised learning tasks. Here’s a breakdown of their differences:

### Regression

1. **Objective**: The goal of regression is to predict a continuous output value.

2. **Output**: The output is a real number (e.g., predicting the price of a house, temperature, stock prices).

3. **Examples**:

   - Predicting the price of a house based on features like size, location, and number of bedrooms.

   - Estimating the amount of rainfall in a given area based on historical weather data.

4. **Common Algorithms**:

   - Linear Regression

   - Polynomial Regression

   - Ridge Regression

   - Lasso Regression

   - Support Vector Regression (SVR)

   - Neural Networks (for regression tasks)

### Classification

1. **Objective**: The goal of classification is to predict a discrete class label.

2. **Output**: The output is a category or class (e.g., classifying an email as spam or not spam, predicting the type of animal in an image).

3. **Examples**:

   - Determining whether an email is spam or not.

   - Classifying a handwritten digit from 0 to 9.

   - Predicting if a tumor is malignant or benign based on medical images.

4. **Common Algorithms**:

   - Logistic Regression

   - Decision Trees

   - Random Forest

   - Support Vector Machines (SVM)

   - k-Nearest Neighbors (k-NN)

   - Neural Networks (for classification tasks)

   - Naive Bayes

### Key Differences

- **Nature of the Problem**:

  - Regression deals with predicting quantities (continuous values).

  - Classification deals with assigning items to predefined categories or classes.

- **Evaluation Metrics**:

  - Regression models are typically evaluated using metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared.

  - Classification models are evaluated using metrics like Accuracy, Precision, Recall, F1-score, and AUC-ROC.

- **Output Format**:

  - Regression produces a numerical output.

  - Classification produces a categorical output.

### Use Cases

- **Regression**:

  - Predicting the future value of a stock.

  - Estimating the cost of a construction project.

  

- **Classification**:

  - Identifying fraudulent transactions.

  - Recognizing objects in images.

By understanding these differences, you can choose the appropriate type of model and algorithm for your specific machine learning task.